Below are some examples of synthesised texture generated using a novel wavelet based texture synthesis algorithm. The example texture is shown in the middle inside the black square and the new synthesised texture is grown around this example texture ``seed''. This algorithm combines the benefits of non-parametric modelling with wavelet based texture analysis. Unlike previous non-parametric approaches the algorithm is scale robust and generates impressive results for a wide range of textures. In addition, because most of the computationally intensive analysis work is performed at the coarse resolution, the algorithm is computationally efficient making it suitable for generating large texture images.

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Below are some examples of synthesised texture generated using a novel wavelet based texture synthesis algorithm. The example texture is shown in the middle inside the black square and the new synthesised texture is grown around this example texture "seed". This algorithm combines the benefits of non-parametric modelling with wavelet based texture analysis. Unlike previous non-parametric approaches the algorithm is scale robust and generates impressive results for a wide range of textures. In addition, because most of the computationally intensive analysis work is performed at the coarse resolution, the algorithm is computationally efficient making it suitable for generating large texture images.

The problem of texture synthesis is a large research area in the computer graphics industry and in image post production and has received much attention in recent years. Given an example texture as a small sub-image, the idea behind a successful texture synthesis algorithm is to create a new (typically larger) image by generating or \textit{synthesising) more texture. This new synthesised texture should be perceptually similar and thus give the impression of being generated from the same underlying statistical process as the example texture.

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The problem of texture synthesis is a large research area in the computer graphics industry and in image post production and has received much attention in recent years. Given an example texture as a small sub-image, the idea behind a successful texture synthesis algorithm is to create a new (typically larger) image by generating or synthesising more texture. This new synthesised texture should be perceptually similar and thus give the impression of being generated from the same underlying statistical process as the example texture.

To further demonstrate the strength of this new approach, an extensive comparison has been done between it and results obtained using a wide variety of previous approaches. The original texture images are taken from the Brodatz collection and the synthesised results obtained using the various approaches are shown below.

Below are some examples of synthesised texture generated using a novel wavelet based texture synthesis algorithm. The example texture is shown in the middle inside the black square and the new synthesised texture is grown around this example texture ``seed''. This algorithm combines the benefits of non-parametric modelling with wavelet based texture analysis. Unlike previous non-parametric approaches the algorithm is scale robust and therefore works for a wide range of textures. In addition, becuase most of the computationally intensive analysis work is performed at the coarse resolution, the algorithm is fast.

to:

Below are some examples of synthesised texture generated using a novel wavelet based texture synthesis algorithm. The example texture is shown in the middle inside the black square and the new synthesised texture is grown around this example texture ``seed''. This algorithm combines the benefits of non-parametric modelling with wavelet based texture analysis. Unlike previous non-parametric approaches the algorithm is scale robust and generates impressive results for a wide range of textures. In addition, because most of the computationally intensive analysis work is performed at the coarse resolution, the algorithm is computationally efficient making it suitable for generating large texture images.

Texture synthesis is an important process in image post production. The idea behind a texture synthesis algorithm is that: given a small input sample of texture, create or synthesis a much later texture image which will be perceived by humans to be the same texture. Two different approaches to this problem have emerged. Parametric methods attempt to model the image with some definable process. These are efficient but do not work well on complicated textures. Non-parametric methods are simpler and rather attempt to measure the probability density function of each individual pixel using just the sample image itself. This approach was popularised by Efros and Leung in 1999. Because of the wide variability in image behaviour, non-parametric approaches have achieved by far the most pleasing results. However, one of the downsides to non-parametric methods is their computational costs and their dependence on scale. To address these problems we have developed a new algorithm which uses wavelet decomposition as a basis for non-parametric texture synthesis. Below are some of the results obtained using the algorithm. A full description of the algorithm is given here.

to:

The problem of texture synthesis is a large research area in the computer graphics industry and in image post production and has received much attention in recent years. Given an example texture as a small sub-image, the idea behind a successful texture synthesis algorithm is to create a new (typically larger) image by generating or \textit{synthesising) more texture. This new synthesised texture should be perceptually similar and thus give the impression of being generated from the same underlying statistical process as the example texture.

Below are some examples of synthesised texture generated using a novel wavelet based texture synthesis algorithm. The example texture is shown in the middle inside the black square and the new synthesised texture is grown around this example texture ``seed''. This algorithm combines the benefits of non-parametric modelling with wavelet based texture analysis. Unlike previous non-parametric approaches the algorithm is scale robust and therefore works for a wide range of textures. In addition, becuase most of the computationally intensive analysis work is performed at the coarse resolution, the algorithm is fast.

Texture synthesis is an important process in image post production. The idea behind a texture synthesis algorithm is that: given a small input sample of texture, create or synthesis a much later texture image which will be perceived by humans to be the same texture. Two different approaches to this problem have emerged. Parametric methods attempt to model the image with some definable process. These are efficient but do not work well on complicated textures. Non-parametric methods are simpler and rather attempt to measure the probability density function of each individual pixel using just the sample image itself. This approach was popularised by Efros and Leung in 1999. Because of the wide variability in image behaviour, non-parametric approaches have achieved by far the most pleasing results. However, one of the downsides to non-parametric methods is their computational costs and their dependence on scale. To address these problems we have developed a new algorithm which uses wavelet decomposition as a basis for non-parametric texture synthesis. Below are some of the results obtained using the algorithm. A full description of the algorithm is given here.

to:

Texture synthesis is an important process in image post production. The idea behind a texture synthesis algorithm is that: given a small input sample of texture, create or synthesis a much later texture image which will be perceived by humans to be the same texture. Two different approaches to this problem have emerged. Parametric methods attempt to model the image with some definable process. These are efficient but do not work well on complicated textures. Non-parametric methods are simpler and rather attempt to measure the probability density function of each individual pixel using just the sample image itself. This approach was popularised by Efros and Leung in 1999. Because of the wide variability in image behaviour, non-parametric approaches have achieved by far the most pleasing results. However, one of the downsides to non-parametric methods is their computational costs and their dependence on scale. To address these problems we have developed a new algorithm which uses wavelet decomposition as a basis for non-parametric texture synthesis. Below are some of the results obtained using the algorithm. A full description of the algorithm is given here.

Texture synthesis is an important process in image post production. The idea behind a texture synthesis algorithm is that: given a small input sample of texture, create or synthesis a much later texture image which will be perceived by humans to be the same texture. Two different approaches to this problem have emerged. Parametric methods attempt to model the image with some definable process. These are efficient but do not work well on complicated textures. Non-parametric methods are simpler and rather attempt to measure the probability density function of each individual pixel using just the sample image itself. This approach was popularised by Efros and Leung in 1999. Because of the wide variability in image behaviour, non-parametric approaches have achieved by far the most pleasing results. However, one of the downsides to non-parametric methods is their computational costs and their dependence on scale. To address these problems we have developed a new algorithm which uses wavelet decomposition as a basis for non-parametric texture synthesis. Below are some of the results obtained using the algorithm. A full description of the algorithm is given here.